Lightweight crop disease identification network based on frequency domain and channel mixing attention and cross-scale semantic fusion

Abstract

Accurate identification of crop diseases is essential for enhancing agricultural productivity; however, it encounters challenges arising from complex field conditions and the constraints of deploying on resource-limited devices. This study aims to develop a lightweight yet accurate framework, referred to as FCDRNet, which integrates feature enhancement and compression techniques to facilitate practical deployment in the field.